Online option price forecasting by using unscented Kalman filters and support vector machines

Authors:
Shian-Chang Huang
Affiliations:
Department of Business Administration, National Changhua University of Education, No. 2, Shi-Da Road, Changhua 500, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black-Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can help investors for reducing their risk in online trading.